A team from DOST-ASTI is developing an image classification technique that helps predict the intensity of tropical cyclones in the country. The team created an experiment by employing a process called “transfer learning” to a Convolutional Neural Network (CNN) model. Transfer learning is a branch of machine learning, which focuses on re-applying a “learned algorithm” to another research problem. Onthe other hand, a CNN is a class of Artificial Neural Networks (ANNs), which, on a nutshell, are computing systems that could be trained to “learn” and perform complex tasks such as identifying and classifying images. When brought together, this “transfer learning” and CNN process could be helpful in forecasting the intensity of tropical cyclones in the Philippines, just by sifting through and analyzing a multitude of satellite infrared images.

In the transfer experiment process, a pre-trained neural network model, called the VGG19 network architecture, was used in recognizing large-scale satellite images provided by geostationary satellites in the Western North Pacific Basin. Originally, the VGG19 was trained to only recognize general visual patterns; but in this study, the VGG19 was re-trained to recognize tropical cyclone images and predict their intensities. The team then observed the performance of the VGG19 neural network model through the assistance of two NVIDIA K80 GPU compute nodes in the Computing and Archiving Research Environment (COARE) facility.

The pre-trained neural network model was found able to successfully implement image classification without the further need to apply more complex and time consuming feature-extraction algorithms from the past. In addition, the model was applied to estimation of tropical cyclone intensity in the country and learn other patterns that could identify a tropical cyclone, such as cloud formation and the presence or absence of a wellformed eye. These algorithmic advancements could enable faster and more timely weather forecasts.